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Assessment of Weather-Related Risk on Chestnut Productivity

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Assessment of Weather-Related Risk on Chestnut Productivity Nat. Hazards Earth Syst. Sci., 11, 2729–2739, 2011 www.nat-hazards-earth-syst-sci.net/11/2729/2011/ Natural Hazards doi:10.5194/nhess-11-2729-2011 and Earth © Author(s) 2011. CC Attribution 3.0 License. System Sciences Assessment of weather-related risk on chestnut productivity M. G. Pereira1,2,3, L. Caramelo1,3, C. Gouveia2,4, J. Gomes-Laranjo1, and M. Magalhaes˜ 5 1Centre for Research and Technology of Agro-Environment and Biological Sciences (CITAB), University of Tras-os-Montes´ and Alto Douro, Vila Real, Portugal 2Instituto Dom Luiz – Universidade de Lisboa, Lisboa, Portugal 3Departamento de F´ısica, Escola de Cienciaˆ e Tecnologia, Universidade de Tras-os-Montes´ e Alto Douro (DF-ECT UTAD) Vila Real, Portugal 4Escola Superior de Tecnologia, Instituto Politecnico´ de Setubal,´ Setubal,´ Portugal 5Departamento de Cienciasˆ Florestais e Arquitectura Paisagista, Escola de Cienciaˆ Agrarias´ e Veterinarias, Universidade de Tras-os-Montes´ e Alto Douro (DCFAP-ECAV UTAD) Vila Real, Portugal Received: 31 January 2011 – Revised: 20 April 2011 – Accepted: 30 May 2011 – Published: 12 October 2011 Abstract. Due to its economic and nutritional value, the 1 Introduction world production of chestnuts is increasing as new stands are being planted in various regions of the world. This work fo- According to FAO statistics (FAO, 2010), Portugal was the cuses on the relation between weather and annual chestnut sixth world’s largest producer in 2008 with 22 000 tons; the production to model the role of weather, to assess the im- world’s largest producers are China (1 000 000 tons), South pacts of climate change and to identify appropriate locations Korea (75 000 tons) Italy and Turkey (55 000 tons), and for new groves. The exploratory analysis of chestnut produc- Japan (26 000 tons), but it should be noted that all these coun- tion time series and the striking increase of production area tries have a much higher land area than Portugal (Bounous, have motivated the use for chestnut productivity. A large set 2002b). The most noteworthy facts from world chestnut pro- of meteorological variables and remote sensing indices were duction trends in the last four decades are: (i) East Asian computed and their role on chestnut productivity evaluated production continues to enlarge, mainly because of the great with composite and correlation analyses. These results al- increase in the contribution of China (from 130 000 tons low for the identification of the variables cluster with a high in the 60s of the 20th century); (ii) a general decrease in correlation and impact on chestnut production. Then, differ- the production in some western European countries (Portu- ent selection methods were used to develop multiple regres- gal, 82 000 tons in the 60s, Spain, France and Italy) and an sion models able to explain a considerable fraction of pro- increase in Turkey (40 000 in the 60s); (iii) new orchards ductivity variance: (i) a simulation model (R2-value = 87 %) have been planted in Europe, North (USA) and South Amer- based on the winter and summer temperature and on spring ica (Brazil and Chile), Australia and New Zealand, due to and summer precipitation variables; and, (ii) a model to pre- the increase of the retail price for quality nuts and pro- dict yearly chestnut productivity (R2-value of 63 %) with five cessed products and by the European Community funding months in advance, combining meteorological variables and programmes (Bounous, 2002b; Gomes-Laranjo et al., 2007). NDVI. Goodness of fit statistic, cross validation and resid- Chestnut trees are also cultivated for their fruit and wood. ual analysis demonstrate the model’s quality, usefulness and With regards to the fruit, it is used in preparations of many consistency of obtained results. recipes due to its dietetic value. Its wood is as strong as oak, but significantly lighter. In fact, results obtained by Ja- cobs et al. (2009) demonstrated that North American chest- nut trees compete favourably in the aboveground allocation of biomass and carbon sequestration ability with any other species in this region. A chestnut agro-ecosystem also pro- vides a habitat for diverse macrofungal species which sup- port a high value of economical activity such as the mush- Correspondence to: M. G. Pereira room harvesting (Baptista et al., 2010). ([email protected]) Published by Copernicus Publications on behalf of the European Geosciences Union. 2730 M. G. Pereira et al.: Assessment of weather-related risk on chestnut productivity In Europe, the most important chestnut specie is Castanea applications in drought monitoring (Vicente-Serrano et al., sativa Mill., one of 13 species from Castanea genus. In 2006; Gouveia et al., 2009), agriculture, crop growth moni- relation to its phenology, bud break happens at the end of toring, yield modelling (Gouveia and Trigo, 2008) and crop April, the flowering period is between June and July, being identification. In particular, the time-series of satellite im- the last phase related to fruit development between August agery efficiently provide a synoptic view of vegetation dy- and October, time for fruit fall. This species, also called namics, namely the chestnut vegetative cycle that may be sweet chestnut, dislikes chalky soil, but appreciats sedimen- used for chestnut management. Phenological information tary or siliceous soils. Their roots tend to decay in poorly is, in fact, essential for decision making during many of the drained soils, which help to explain why they prosper on phases of growing, namely on management planning, pest hills and mountainsides. The European chestnut is cultivated and disease control (Gouveia et al., 2011). In this context, for its nuts and wood and can be found on acidic to neutral several vegetation indices have been used in order to describe soils, influenced by an oceanic climate which is characterised the phenology, namely the Normalized Difference Vegetation by annual mean values of sunlight spanning between 2400 Index (NDVI) as derived from remote-sensed information. and 2600 h and rainfall ranging between 600 and 1500 mm, The NDVI was designed to capture the contrast between red mean annual temperature between 9 and 13 ◦C, 27 ◦C being and near-infrared reflection of solar radiation by vegetation, the mean of the maximal temperature (Heiniger and Coned- and is an indicator of the amount of green leaf area (Asrar et era, 1992; Gomes-Laranjo et al., 2008). According to Dinis al., 1984; Myneni et al., 1995). Despite its simplicity, NDVI et al. (2011), chestnut regions must have 1900–2200 ◦D be- has been widely used in studies of vegetation phenology and tween May and October. The degree-days (◦D) is the sum of interannual variability of vegetation greenness (Gouveia et the temperature values in degrees Celsius with a base temper- al., 2008). ature of 6 ◦C (Cesaraccio et al., 2001; Dinis et al., 2011). Ac- This work aims to identify the favourable/adverse weather cordingly, in the Iberian Peninsula, this edaphoclimatic situ- conditions to chestnut production as well as helping to as- ation can be observed since sea level on seacoast regions to sess risk and to identify appropriate measures for adaptation mountainous regions (between 600 and 1000 m a.s.l.) in the to climate change. In this sense, the three specific objectives inner part of the continent. The influence of temperature and of this work are: (i) to characterise the chestnuts production radiation on photosynthesis productivity in chestnut popula- in Portugal; (ii) to quantify the role of weather and climate tions in Northeast Portugal was analysed by Gomes-Laranjo on chestnut production; and, (iii) to develop simulation and et al. (2006). Maximum photosynthetic activity occurs at prediction models of chestnut production based on meteoro- 24–28 ◦C for adult trees, but exhibits termoinhibition when logical variables and vegetation indices. the air temperature is above 32 ◦C, which is frequent dur- ing summer (Gomes-Laranjo et al., 2006, 2008). All species of plants are dependent on the weather with regards to their 2 Study area and datasets production. However, only a few number of works have been This work uses chestnut production, vegetation index and published on weather dependence of chestnut production and meteorological datasets which cover the 1982–2006 period none of references found on this subject intend to quantify and includes: and model portuguese chestnut production. Wilczynski and Podlaski (2007) concluded that the radial growth of horse – annual values of the total chestnut production and the chestnut (Aesculus hippocastanum L.) is positively related production area in Portugal, provided by the Portuguese to high air temperature of August and during the previous National Institute of Statistics (INE, 2010) (http://www. winter and negatively related to excessive precipitation in ine.pt); August. The growing season is defined as the period of – daily values of several meteorological variables reg- time when the mean 24-h temperature is greater than 5 ◦C. istered in the Braganc¸a weather station, located at Fernandez-L´ opez´ et al. (2005) study the geographic differen- 41◦4702800 N and 6◦4504300 W, 740 m a.s.l. (Fig. 1), tiation in adaptive traits of the wild chestnut populations in namely maximum, minimum and mean temperature, Spain resorting to climate data (e.g., temperature variation, wind speed, total (rain and/or melted snow) precipi- summer precipitation/droughts and the temperature of the tation, as well as a set of weather parameters indica- warmest month) and adding evidence of the role of some me- tive of the occurrence of hail, snow, fog and storm. teorological variables, namely frost during bud break, mean This data was obtained from the Meteored web site temperature of the warmest month, summer precipitation and (METEORED, 2009); drought, creating a xerothermic index. The influence of tem- perature and radiation on the photosynthesis productivity in – Monthly Normalized Difference Vegetation Index chestnut populations in Northeast Portugal was analysed by (NDVI) dataset, at 8-km resolution, from the Advanced Almeida et al.
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